Latent Dirichlet Allocation (LDA) Implementation
├── Introduction
│   └── Overview of LDA
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Preparing the Dataset
├── Implementing LDA
│   ├── Data Preprocessing
│   ├── Model Training
│   └── Identifying Topics
├── Visualization
│   └── Topic Distribution Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of LDA

• Latent Dirichlet Allocation (LDA) is a type of probabilistic model that is used for uncovering the underlying topics in a collection of documents.

2. Setting Up the Environment

Importing Libraries

pythonCopy code
# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

Preparing the Dataset

# Python code to prepare a sample dataset
# (Assuming 'documents' is a list of text documents)
documents = ["Text of document 1", "Text of document 2", "Text of document 3", ...]

3. Implementing LDA

Data Preprocessing

# Python code for data preprocessing
vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')
doc_term_matrix = vectorizer.fit_transform(documents)

Model Training

# Python code to train the LDA model
lda = LatentDirichletAllocation(n_components=10, random_state=0)
lda.fit(doc_term_matrix)

Identifying Topics

# Python code to display identified topics
feature_names = vectorizer.get_feature_names()
for topic_idx, topic in enumerate(lda.components_):
    print(f"Topic #{topic_idx+1}:")
    print(" ".join([feature_names[i] for i in topic.argsort()[:-11:-1]]))

4. Visualization

Topic Distribution Visualization